At 4:57 +1000 26/7/16, Andy Farkas wrote:
>A really nice and simple overview of machine learning:
>https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.pigplooz9
To the sceptics among us, it provides a nice, easy way to develop the critique
that machine learning badly needs.
For example:
>"Machine learning" is an umbrella term covering lots of ... generic algorithms
>[that can tell you something interesting about a set of data without you
>having to write any custom code specific to the problem]
This begs such questions as:
- how big is the set?
- what parts of the real world do they correspond to sufficiently
accurately to be useful?
- how is the choice made as to which algorithm to use in which context?
- how is the size and content of a sufficient training-set determined?
- how are 'answers' tested against the real-world?
- how is an audit performed?
>In supervised learning, you are letting the computer work out that
>relationship for you. And once you know what math was required to solve this
>specific set of problems, you could answer to any other problem of the same
>type!
If that meant "you can generate *an* answer", all would be well.
But the presumption is far too easily made that it's *the* answer.
> ... unsupervised learning is becoming increasingly important as the
> algorithms get better because it can be used without having to label the data
> with the correct answer
Oh dear, now the risk of blind presumption of 'truth' is no longer just a
sceptic's fantasy. Geitgey now believes his own mythology.
It's particularly hilarious given that his chosen example is property
valuation, and 'value' is highly context-dependent and not a topic to which any
notion of 'truth' / 'correct answer' applies.
And his aside addressed to sceptics like me missed the point entirely:
>Side note for pedants: There are lots of other types of machine learning
>algorithms. But this is a pretty good place to start.
>Of course if you are reading this 50 years in the future and we've figured out
>the algorithm for Strong AI, then this whole post will all seem a little
>quaint.
It's quaint, but not for the reason he thinks. The quaintness lies in the
naive belief that there's such a thing as "the algorithm for Strong AI".
> ... you've just written a function that you don't really understand but that
> you can prove will work
The notion that you can 'prove that something doesn't work' is tenable.
But the notion that you can 'prove that something *does* work' is delusional.
What works on any particular training-set (nomatter how large) may or may not
work for the next instance.
And his very next example suggests that the algorithm that supports guesses
about a house's market-value (a basically harmless application) is the one you
want to embed in your autonomous car (an essentially dangerous application).
>Then you are using that equation to guess the sales price of houses you've
>never seen before based where that house would appear on your line. It's a
>really powerful idea and you can solve "real" problems with it.
The leap from 'guess' to 'solve' is breathtaking.
> ... it's important to remember that machine learning only works if the
> problem is actually solvable with the data that you have
Ah, could he be about to become wise?
>For example, if you build a model that predicts home prices based on the type
>of potted plants in each house, it's never going to work. There just isn't any
>kind of relationship between the potted plants in each house and the home's
>sale price. So no matter how hard it tries, the computer can never deduce a
>relationship between the two.
But he's completely missed the point that:
(a) there are many, many circumstances in which correlations can be found
(b) the scheme implicitly confuses correlation with causality ("predicts")
>In my mind, the biggest problem with machine learning right now is that it
>mostly lives in the world of academia and commercial research groups
And he's got that arse-about as well. Keeping AI, sub-sets like ML, and naive
people like him, in laboratories is the best thing we can do.
Nope, I haven't yet read Parts 2-4, but there are some rather more useful
things I need to do this morning than deconstruct a silly belief system.
</tetchy>
--
Roger Clarke http://www.rogerclarke.com/
Xamax Consultancy Pty Ltd 78 Sidaway St, Chapman ACT 2611 AUSTRALIA
Tel: +61 2 6288 6916 http://about.me/roger.clarke
mailto:[email protected] http://www.xamax.com.au/
Visiting Professor in the Faculty of Law University of N.S.W.
Visiting Professor in Computer Science Australian National University
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